Modified Regularization for High-dimensional Data Decomposition
Bibliographic record
Abstract
With the increased dimensionality of datasets, high-dimensional data decomposition models have become essential data analysis tools. However, the decomposition method usually suffers from the overfitting problem and, consequently, cannot achieve state-of-the-art performance. This motivates the introduction of various regularization terms. The commonly applied Ridge regression has limited applicability for the asperity dataset and reduces performance for sparse data, while the Lasso regression has higher efficiency in the sparse dataset. To address this challenge, we propose a modified regularization term designed by integrating both the Lasso and Ridge regressions. The different roles of these two regressions are analyzed. By adjusting the weights of the regression in the regularization term, the existing decomposition method can be applied to the dataset with different degrees of sparsity. The experiments show that the modified regularization term yields consistent improvement in the performance of existing benchmarks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".